Performance analysis roundtrip: automatic generation of performance models and results feedback using cross-model trace links
Bibliographic record
Abstract
This paper proposes an approach for performance analysis roundtrip in the context of model-driven engineering (MDE) of real-time distributed and embedded systems. The starting point is a UML software model with MARTE performance annotations, such as performance requirements and resource demands. The source software model is automatically transformed into a Layered Queueing Network (LQN) performance model. We developed the transformation with Epsilon, a family of languages for model-to-model transformation, model validation and model management. Using specialized languages helped us create a more compact transformation, easier to understand and maintain than transformations developed with general purpose languages, such as Java. Beside the performance model, the transformation also generates a traceability model containing trace links between mapped elements of the software and performance model. After solving the performance model with an existing solver, the performance results are fed back to the software model by following in reverse the cross-model trace links. The software developers can see the performance results as MARTE stereotype attributes, using a standard UML editor. The approach is illustrated by applying it to an e-commerce application.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".